|
|
| BIL5040 | Computer Vision | 3+0+0 | ECTS:7.5 | | Year / Semester | Spring Semester | | Level of Course | Second Cycle | | Status | Elective | | Department | DEPARTMENT of COMPUTER ENGINEERING | | Prerequisites and co-requisites | None | | Mode of Delivery | Face to face | | Contact Hours | 14 weeks - 3 hours of lectures per week | | Lecturer | Prof. Dr. Murat EKİNCİ | | Co-Lecturer | None | | Language of instruction | Turkish | | Professional practise ( internship ) | None | | | | The aim of the course: | | The principal objectives of this course continue to be to provide an introduction to basic concepts and methodologies for computer vision, and to develop a foundation that can be used as the basis for further study and research in this field. |
| Programme Outcomes | CTPO | TOA | | Upon successful completion of the course, the students will be able to : | | | | PO - 1 : | provide an introduction to basic concepts and methodologies for image processing and computer vision, | 1 - 3 - 4 - 5 - 8 - 10 - 11 | 1 | | PO - 2 : | develop a foundation that can be used as the basis for further study and research in this field. | 1 - 2 - 3 - 4 - 5 - 8 - 10 - 11 - 14 - 15 | 1,2 | | PO - 3 : | achieve simple algorithms for different pattern recognition research | 1 - 3 - 4 - 5 - 8 - 10 - 11 - 13 - 14 - 15 | 1,2 | | PO - 4 : | create computer vision based approach for different research in other disciplines | 1 - 4 - 7 - 8 - 10 - 11 - 14 - 15 | 2 | | CTPO : Contribution to programme outcomes, TOA :Type of assessment (1: written exam, 2: Oral exam, 3: Homework assignment, 4: Laboratory exercise/exam, 5: Seminar / presentation, 6: Term paper), PO : Learning Outcome | | |
| Image Pre-processing; Local pre-processing, and edge detectors; Thresholding based image segmentation; Edge-based, region merging-splitting based image segmentation; Mathing methods; Texture feature extraction and statistical texture recognition; Feature Classification; Shape representation and description; Feature extraction and statistical pattern recognition; Image classification and understanding; Mathematical morpholgy; Vision geometry and 3D vision; Motion analysis. |
| |
| Course Syllabus | | Week | Subject | Related Notes / Files | | Week 1 | Image Pre-processing | | | Week 2 | Local pre-processing, and edge detectors | | | Week 3 | Thresholding based image segmentation | | | Week 4 | Edge-based, region merging-splitting based image segmentation; | | | Week 5 | Segmentation as Clustering and Matching methods, | | | Week 6 | Texture feature extraction and statistical texture recognition, | | | Week 7 | Basic Feature Classification | | | Week 8 | Shape representation and description | | | Week 9 | Mid-term exam | | | Week 10 | Feature extraction and statistical pattern recognition | | | Week 11 | Image classification and understanding, | | | Week 12 | Mathematical morpholgy | | | Week 13 | Vision geometry and 3D vision | | | Week 14 | Principal Component Analysis and Fisher Discrement Analysis in Pattern Recognition | | | Week 15 | Pattern Classification Algorithms | | | Week 16 | End-of-term exam | | | |
| 1 | Milan Sonka, Vaclav Hlavac, Roger Boyle, 1999, Image Processing, Analysis, and Machine Vision, Second Edition, PWS Puıblishing, | | | |
| 1 | Rafael C. Gonzales, Richard E. Woods, 1998, Digital Image Processing, Addison-Wesley Publishing Company | | | 2 | Gerhard X. Ritter, Joseph N. Wilson, 2001, Handbook of Computer Vision Algorithms in Image Algebra, CRC Press | | | |
| Method of Assessment | | Type of assessment | Week No | Date | Duration (hours) | Weight (%) | | Mid-term exam | 9 | 12/04/2013 | 2 | 30 | | Project | 15 | 24/05/2013 | 2 | 20 | | End-of-term exam | 16 | 07/06/2013 | 2 | 50 | | |
| Student Work Load and its Distribution | | Type of work | Duration (hours pw) | No of weeks / Number of activity | Hours in total per term | | Yüz yüze eğitim | 3 | 14 | 42 | | Sınıf dışı çalışma | 4 | 14 | 56 | | Arasınav için hazırlık | 12 | 1 | 12 | | Arasınav | 2 | 1 | 2 | | Proje | 5 | 14 | 70 | | Dönem sonu sınavı için hazırlık | 15 | 1 | 15 | | Dönem sonu sınavı | 3 | 1 | 3 | | Total work load | | | 200 |
|